| Title | Material Characterization for Magnetic Soft Robots |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Da Veiga, Tomás, Chandler, James H., Pittiglio, Giovanni, Lloyd, Peter, Holdar, Mohammad, Onaizah, Onaizah, Alazmani, Ali, Valdastri, Pietro |
| Conference Name | 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft) |
| Keywords | compositionality, Control, cyber physical systems, Internet of Things, Learning for Soft Robots, Load modeling, Magnetization, Magnetoelasticity, Modeling, Particle measurements, Predictive models, pubcrawl, remanence, Resiliency, Soft magnetic materials, Soft Robot Materials and Design, Soft robotics, Steerable Catheters/Needles, Surgical Robotics |
| Abstract | Magnetic soft robots are increasingly popular as they provide many advantages such as miniaturization and tetherless control that are ideal for applications inside the human body or in previously inaccessible locations.While non-magnetic elastomers have been extensively characterized and modelled for optimizing the fabrication of soft robots, a systematic material characterization of their magnetic counterparts is still missing. In this paper, commonly employed magnetic materials made out of Ecoflex(tm) 00-30 and Dragon Skin(tm) 10 with different concentrations of NdFeB microparticles were mechanically and magnetically characterized. The magnetic materials were evaluated under uniaxial tensile testing and their behavior analyzed through linear and hyperelastic model comparison. To determine the corresponding magnetic properties, we present a method to determine the magnetization vector, and magnetic remanence, by means of a force and torque load cell and large reference permanent magnet; demonstrating a high level of accuracy. Furthermore, we study the influence of varied magnitude impulse magnetizing fields on the resultant magnetizations. In combination, by applying improved, material-specific mechanical and magnetic properties to a 2-segment discrete magnetic robot, we show the potential to reduce simulation errors from 8.5% to 5.4%. |
| DOI | 10.1109/RoboSoft51838.2021.9479189 |
| Citation Key | da_veiga_material_2021 |
Material Characterization for Magnetic Soft Robots